Search NASA⌕ Search

DOE OSTI · 4565438

Dynamic Characteristics of Multistory Buildings

Abstract

In order to evaluate unit stresses or to estimate possible damage in multistory buildings from earthquake or ground motion from underground nuclear explosion it is essential to determine how much of the dynamic response at any level is due to various types of freedom. The effects of shear deformation between floors, joint rotation, over-all flexure, and ground compliance are considered. Joint rotation and over-all flexure are evaluated over a wide range of building characteristics with simply determined indices. Further, a period synthesis concept and a pseudo-stiffness procedure are proposed to enable the determination of periods, mode shapes and stiffnesses with consideration of joint rotation, over-all flexure and ground compliance while performing simple labor-saving analyses for assumed rigid-floor shear buildings. The first three modes of vibration are considered in elastic free vibration. The buildings and models are symmetric in plan without torsional coupling.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Blume, John A.. 2021-02-01. Dynamic Characteristics of Multistory Buildings. https://doi.org/10.1061/jsdeag.0001880

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗